{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "view-in-github"
   },
   "source": [
    "<a target=\"_blank\" href=\"https://colab.research.google.com/github/AI4Finance-Foundation/FinRL-Tutorials/blob/master/4-Optimization/Vectorbt_connectToAgent.ipynb\">\n",
    "  <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/>\n",
    "</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gXaoZs2lh1hi"
   },
   "source": [
    "# Pass vectorbt data to the agent\n",
    "\n",
    "* **Pytorch Version** \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "w9A8CN5R5PuZ"
   },
   "outputs": [],
   "source": [
    "from finrl import config\n",
    "from finrl import config_tickers\n",
    "import os\n",
    "if not os.path.exists(\"./\" + config.DATA_SAVE_DIR):\n",
    "    os.makedirs(\"./\" + config.DATA_SAVE_DIR)\n",
    "if not os.path.exists(\"./\" + config.TRAINED_MODEL_DIR):\n",
    "    os.makedirs(\"./\" + config.TRAINED_MODEL_DIR)\n",
    "if not os.path.exists(\"./\" + config.TENSORBOARD_LOG_DIR):\n",
    "    os.makedirs(\"./\" + config.TENSORBOARD_LOG_DIR)\n",
    "if not os.path.exists(\"./\" + config.RESULTS_DIR):\n",
    "    os.makedirs(\"./\" + config.RESULTS_DIR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "lPqeTTwoh1hn",
    "outputId": "10b08480-3a0c-4826-8e51-f94ce97ab84a"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/mnt/recoverData/linuxProgram/workspace/Finrl_python3.9/__pypackages__/3.9/lib/pyfolio/pos.py:26: UserWarning: Module \"zipline.assets\" not found; multipliers will not be applied to position notionals.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "# matplotlib.use('Agg')\n",
    "import datetime\n",
    "\n",
    "%matplotlib inline\n",
    "from finrl.meta.preprocessor.yahoodownloader import YahooDownloader\n",
    "from finrl.meta.preprocessor.preprocessors import FeatureEngineer, data_split\n",
    "from finrl.meta.env_stock_trading.env_stocktrading import StockTradingEnv\n",
    "from finrl.agents.stablebaselines3.models import DRLAgent\n",
    "from finrl.meta.data_processor import DataProcessor\n",
    "\n",
    "from finrl.plot import backtest_stats, backtest_plot, get_daily_return, get_baseline\n",
    "from pprint import pprint\n",
    "\n",
    "import sys\n",
    "sys.path.append(\"../FinRL-Library\")\n",
    "\n",
    "import itertools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/mnt/recoverData/linuxProgram/workspace/Finrl_python3.9/__pypackages__/3.9/lib/dateparser/date_parser.py:35: PytzUsageWarning: The localize method is no longer necessary, as this time zone supports the fold attribute (PEP 495). For more details on migrating to a PEP 495-compliant implementation, see https://pytz-deprecation-shim.readthedocs.io/en/latest/migration.html\n",
      "  date_obj = stz.localize(date_obj)\n",
      "/mnt/recoverData/linuxProgram/workspace/Finrl_python3.9/__pypackages__/3.9/lib/dateparser/date_parser.py:35: PytzUsageWarning: The localize method is no longer necessary, as this time zone supports the fold attribute (PEP 495). For more details on migrating to a PEP 495-compliant implementation, see https://pytz-deprecation-shim.readthedocs.io/en/latest/migration.html\n",
      "  date_obj = stz.localize(date_obj)\n",
      "/mnt/recoverData/linuxProgram/workspace/Finrl_python3.9/__pypackages__/3.9/lib/dateparser/date_parser.py:35: PytzUsageWarning: The localize method is no longer necessary, as this time zone supports the fold attribute (PEP 495). For more details on migrating to a PEP 495-compliant implementation, see https://pytz-deprecation-shim.readthedocs.io/en/latest/migration.html\n",
      "  date_obj = stz.localize(date_obj)\n",
      "/mnt/recoverData/linuxProgram/workspace/Finrl_python3.9/__pypackages__/3.9/lib/dateparser/date_parser.py:35: PytzUsageWarning: The localize method is no longer necessary, as this time zone supports the fold attribute (PEP 495). For more details on migrating to a PEP 495-compliant implementation, see https://pytz-deprecation-shim.readthedocs.io/en/latest/migration.html\n",
      "  date_obj = stz.localize(date_obj)\n",
      "/mnt/recoverData/linuxProgram/workspace/Finrl_python3.9/__pypackages__/3.9/lib/dateparser/date_parser.py:35: PytzUsageWarning: The localize method is no longer necessary, as this time zone supports the fold attribute (PEP 495). For more details on migrating to a PEP 495-compliant implementation, see https://pytz-deprecation-shim.readthedocs.io/en/latest/migration.html\n",
      "  date_obj = stz.localize(date_obj)\n",
      "/mnt/recoverData/linuxProgram/workspace/Finrl_python3.9/__pypackages__/3.9/lib/dateparser/date_parser.py:35: PytzUsageWarning: The localize method is no longer necessary, as this time zone supports the fold attribute (PEP 495). For more details on migrating to a PEP 495-compliant implementation, see https://pytz-deprecation-shim.readthedocs.io/en/latest/migration.html\n",
      "  date_obj = stz.localize(date_obj)\n",
      "/mnt/recoverData/linuxProgram/workspace/Finrl_python3.9/__pypackages__/3.9/lib/dateparser/date_parser.py:35: PytzUsageWarning: The localize method is no longer necessary, as this time zone supports the fold attribute (PEP 495). For more details on migrating to a PEP 495-compliant implementation, see https://pytz-deprecation-shim.readthedocs.io/en/latest/migration.html\n",
      "  date_obj = stz.localize(date_obj)\n",
      "/mnt/recoverData/linuxProgram/workspace/Finrl_python3.9/__pypackages__/3.9/lib/dateparser/date_parser.py:35: PytzUsageWarning: The localize method is no longer necessary, as this time zone supports the fold attribute (PEP 495). For more details on migrating to a PEP 495-compliant implementation, see https://pytz-deprecation-shim.readthedocs.io/en/latest/migration.html\n",
      "  date_obj = stz.localize(date_obj)\n",
      "/mnt/recoverData/linuxProgram/workspace/Finrl_python3.9/__pypackages__/3.9/lib/dateparser/date_parser.py:35: PytzUsageWarning: The localize method is no longer necessary, as this time zone supports the fold attribute (PEP 495). For more details on migrating to a PEP 495-compliant implementation, see https://pytz-deprecation-shim.readthedocs.io/en/latest/migration.html\n",
      "  date_obj = stz.localize(date_obj)\n",
      "/mnt/recoverData/linuxProgram/workspace/Finrl_python3.9/__pypackages__/3.9/lib/dateparser/date_parser.py:35: PytzUsageWarning: The localize method is no longer necessary, as this time zone supports the fold attribute (PEP 495). For more details on migrating to a PEP 495-compliant implementation, see https://pytz-deprecation-shim.readthedocs.io/en/latest/migration.html\n",
      "  date_obj = stz.localize(date_obj)\n",
      "/mnt/recoverData/linuxProgram/workspace/Finrl_python3.9/__pypackages__/3.9/lib/dateparser/date_parser.py:35: PytzUsageWarning: The localize method is no longer necessary, as this time zone supports the fold attribute (PEP 495). For more details on migrating to a PEP 495-compliant implementation, see https://pytz-deprecation-shim.readthedocs.io/en/latest/migration.html\n",
      "  date_obj = stz.localize(date_obj)\n",
      "/mnt/recoverData/linuxProgram/workspace/Finrl_python3.9/__pypackages__/3.9/lib/dateparser/date_parser.py:35: PytzUsageWarning: The localize method is no longer necessary, as this time zone supports the fold attribute (PEP 495). For more details on migrating to a PEP 495-compliant implementation, see https://pytz-deprecation-shim.readthedocs.io/en/latest/migration.html\n",
      "  date_obj = stz.localize(date_obj)\n",
      "/mnt/recoverData/linuxProgram/workspace/Finrl_python3.9/__pypackages__/3.9/lib/dateparser/date_parser.py:35: PytzUsageWarning: The localize method is no longer necessary, as this time zone supports the fold attribute (PEP 495). For more details on migrating to a PEP 495-compliant implementation, see https://pytz-deprecation-shim.readthedocs.io/en/latest/migration.html\n",
      "  date_obj = stz.localize(date_obj)\n",
      "/mnt/recoverData/linuxProgram/workspace/Finrl_python3.9/__pypackages__/3.9/lib/dateparser/date_parser.py:35: PytzUsageWarning: The localize method is no longer necessary, as this time zone supports the fold attribute (PEP 495). For more details on migrating to a PEP 495-compliant implementation, see https://pytz-deprecation-shim.readthedocs.io/en/latest/migration.html\n",
      "  date_obj = stz.localize(date_obj)\n",
      "/mnt/recoverData/linuxProgram/workspace/Finrl_python3.9/__pypackages__/3.9/lib/dateparser/date_parser.py:35: PytzUsageWarning: The localize method is no longer necessary, as this time zone supports the fold attribute (PEP 495). For more details on migrating to a PEP 495-compliant implementation, see https://pytz-deprecation-shim.readthedocs.io/en/latest/migration.html\n",
      "  date_obj = stz.localize(date_obj)\n",
      "/mnt/recoverData/linuxProgram/workspace/Finrl_python3.9/__pypackages__/3.9/lib/dateparser/date_parser.py:35: PytzUsageWarning: The localize method is no longer necessary, as this time zone supports the fold attribute (PEP 495). For more details on migrating to a PEP 495-compliant implementation, see https://pytz-deprecation-shim.readthedocs.io/en/latest/migration.html\n",
      "  date_obj = stz.localize(date_obj)\n",
      "/mnt/recoverData/linuxProgram/workspace/Finrl_python3.9/__pypackages__/3.9/lib/dateparser/date_parser.py:35: PytzUsageWarning: The localize method is no longer necessary, as this time zone supports the fold attribute (PEP 495). For more details on migrating to a PEP 495-compliant implementation, see https://pytz-deprecation-shim.readthedocs.io/en/latest/migration.html\n",
      "  date_obj = stz.localize(date_obj)\n",
      "/mnt/recoverData/linuxProgram/workspace/Finrl_python3.9/__pypackages__/3.9/lib/dateparser/date_parser.py:35: PytzUsageWarning: The localize method is no longer necessary, as this time zone supports the fold attribute (PEP 495). For more details on migrating to a PEP 495-compliant implementation, see https://pytz-deprecation-shim.readthedocs.io/en/latest/migration.html\n",
      "  date_obj = stz.localize(date_obj)\n",
      "/mnt/recoverData/linuxProgram/workspace/Finrl_python3.9/__pypackages__/3.9/lib/dateparser/date_parser.py:35: PytzUsageWarning: The localize method is no longer necessary, as this time zone supports the fold attribute (PEP 495). For more details on migrating to a PEP 495-compliant implementation, see https://pytz-deprecation-shim.readthedocs.io/en/latest/migration.html\n",
      "  date_obj = stz.localize(date_obj)\n",
      "/mnt/recoverData/linuxProgram/workspace/Finrl_python3.9/__pypackages__/3.9/lib/dateparser/date_parser.py:35: PytzUsageWarning: The localize method is no longer necessary, as this time zone supports the fold attribute (PEP 495). For more details on migrating to a PEP 495-compliant implementation, see https://pytz-deprecation-shim.readthedocs.io/en/latest/migration.html\n",
      "  date_obj = stz.localize(date_obj)\n",
      "/mnt/recoverData/linuxProgram/workspace/Finrl_python3.9/__pypackages__/3.9/lib/dateparser/date_parser.py:35: PytzUsageWarning: The localize method is no longer necessary, as this time zone supports the fold attribute (PEP 495). For more details on migrating to a PEP 495-compliant implementation, see https://pytz-deprecation-shim.readthedocs.io/en/latest/migration.html\n",
      "  date_obj = stz.localize(date_obj)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/mnt/recoverData/linuxProgram/workspace/Finrl_python3.9/__pypackages__/3.9/lib/dateparser/date_parser.py:35: PytzUsageWarning: The localize method is no longer necessary, as this time zone supports the fold attribute (PEP 495). For more details on migrating to a PEP 495-compliant implementation, see https://pytz-deprecation-shim.readthedocs.io/en/latest/migration.html\n",
      "  date_obj = stz.localize(date_obj)\n",
      "/mnt/recoverData/linuxProgram/workspace/Finrl_python3.9/__pypackages__/3.9/lib/dateparser/date_parser.py:35: PytzUsageWarning: The localize method is no longer necessary, as this time zone supports the fold attribute (PEP 495). For more details on migrating to a PEP 495-compliant implementation, see https://pytz-deprecation-shim.readthedocs.io/en/latest/migration.html\n",
      "  date_obj = stz.localize(date_obj)\n",
      "/mnt/recoverData/linuxProgram/workspace/Finrl_python3.9/__pypackages__/3.9/lib/dateparser/date_parser.py:35: PytzUsageWarning: The localize method is no longer necessary, as this time zone supports the fold attribute (PEP 495). For more details on migrating to a PEP 495-compliant implementation, see https://pytz-deprecation-shim.readthedocs.io/en/latest/migration.html\n",
      "  date_obj = stz.localize(date_obj)\n",
      "/mnt/recoverData/linuxProgram/workspace/Finrl_python3.9/__pypackages__/3.9/lib/dateparser/date_parser.py:35: PytzUsageWarning: The localize method is no longer necessary, as this time zone supports the fold attribute (PEP 495). For more details on migrating to a PEP 495-compliant implementation, see https://pytz-deprecation-shim.readthedocs.io/en/latest/migration.html\n",
      "  date_obj = stz.localize(date_obj)\n",
      "/mnt/recoverData/linuxProgram/workspace/Finrl_python3.9/__pypackages__/3.9/lib/dateparser/date_parser.py:35: PytzUsageWarning: The localize method is no longer necessary, as this time zone supports the fold attribute (PEP 495). For more details on migrating to a PEP 495-compliant implementation, see https://pytz-deprecation-shim.readthedocs.io/en/latest/migration.html\n",
      "  date_obj = stz.localize(date_obj)\n",
      "/mnt/recoverData/linuxProgram/workspace/Finrl_python3.9/__pypackages__/3.9/lib/dateparser/date_parser.py:35: PytzUsageWarning: The localize method is no longer necessary, as this time zone supports the fold attribute (PEP 495). For more details on migrating to a PEP 495-compliant implementation, see https://pytz-deprecation-shim.readthedocs.io/en/latest/migration.html\n",
      "  date_obj = stz.localize(date_obj)\n",
      "/mnt/recoverData/linuxProgram/workspace/Finrl_python3.9/__pypackages__/3.9/lib/dateparser/date_parser.py:35: PytzUsageWarning: The localize method is no longer necessary, as this time zone supports the fold attribute (PEP 495). For more details on migrating to a PEP 495-compliant implementation, see https://pytz-deprecation-shim.readthedocs.io/en/latest/migration.html\n",
      "  date_obj = stz.localize(date_obj)\n",
      "/mnt/recoverData/linuxProgram/workspace/Finrl_python3.9/__pypackages__/3.9/lib/dateparser/date_parser.py:35: PytzUsageWarning: The localize method is no longer necessary, as this time zone supports the fold attribute (PEP 495). For more details on migrating to a PEP 495-compliant implementation, see https://pytz-deprecation-shim.readthedocs.io/en/latest/migration.html\n",
      "  date_obj = stz.localize(date_obj)\n",
      "/mnt/recoverData/linuxProgram/workspace/Finrl_python3.9/__pypackages__/3.9/lib/dateparser/date_parser.py:35: PytzUsageWarning: The localize method is no longer necessary, as this time zone supports the fold attribute (PEP 495). For more details on migrating to a PEP 495-compliant implementation, see https://pytz-deprecation-shim.readthedocs.io/en/latest/migration.html\n",
      "  date_obj = stz.localize(date_obj)\n",
      "/mnt/recoverData/linuxProgram/workspace/Finrl_python3.9/__pypackages__/3.9/lib/vectorbt/data/base.py:527: UserWarning: Symbols have mismatching index. Setting missing data points to NaN.\n",
      "  data = cls.align_index(data, missing=missing_index)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (3231, 8)\n",
      "Successfully added vix\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/mnt/recoverData/linuxProgram/workspace/Finrl_python3.9/finrl/finrl_meta/preprocessor/yahoodownloader.py:51: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data_df = data_df.append(temp_df)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully added turbulence index\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>tic</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>rsi_30</th>\n",
       "      <th>ma_30</th>\n",
       "      <th>ma_60</th>\n",
       "      <th>vix</th>\n",
       "      <th>turbulence</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2008-12-31</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>2.625211</td>\n",
       "      <td>2.679260</td>\n",
       "      <td>2.605973</td>\n",
       "      <td>2.606278</td>\n",
       "      <td>607541200.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2008-12-31</td>\n",
       "      <td>AMGN</td>\n",
       "      <td>43.437676</td>\n",
       "      <td>44.281939</td>\n",
       "      <td>43.399647</td>\n",
       "      <td>43.924458</td>\n",
       "      <td>6287200.0</td>\n",
       "      <td>59.908163</td>\n",
       "      <td>2.780335</td>\n",
       "      <td>2.841795</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2008-12-31</td>\n",
       "      <td>AXP</td>\n",
       "      <td>14.442319</td>\n",
       "      <td>15.069198</td>\n",
       "      <td>14.394098</td>\n",
       "      <td>14.908460</td>\n",
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       "      <td>40.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2008-12-31</td>\n",
       "      <td>BA</td>\n",
       "      <td>31.195798</td>\n",
       "      <td>32.290913</td>\n",
       "      <td>31.128291</td>\n",
       "      <td>32.005882</td>\n",
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       "      <td>40.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2008-12-31</td>\n",
       "      <td>CAT</td>\n",
       "      <td>29.963740</td>\n",
       "      <td>30.923676</td>\n",
       "      <td>29.963740</td>\n",
       "      <td>30.628838</td>\n",
       "      <td>6277400.0</td>\n",
       "      <td>59.908163</td>\n",
       "      <td>2.780335</td>\n",
       "      <td>2.841795</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93665</th>\n",
       "      <td>2021-10-28</td>\n",
       "      <td>UNH</td>\n",
       "      <td>448.595319</td>\n",
       "      <td>453.447669</td>\n",
       "      <td>448.119976</td>\n",
       "      <td>451.011597</td>\n",
       "      <td>1672600.0</td>\n",
       "      <td>64.621205</td>\n",
       "      <td>412.388166</td>\n",
       "      <td>411.987484</td>\n",
       "      <td>16.530001</td>\n",
       "      <td>75.498292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93666</th>\n",
       "      <td>2021-10-28</td>\n",
       "      <td>V</td>\n",
       "      <td>218.092662</td>\n",
       "      <td>218.828761</td>\n",
       "      <td>207.906710</td>\n",
       "      <td>208.732330</td>\n",
       "      <td>23199400.0</td>\n",
       "      <td>42.595810</td>\n",
       "      <td>225.210895</td>\n",
       "      <td>227.487207</td>\n",
       "      <td>16.530001</td>\n",
       "      <td>75.498292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93667</th>\n",
       "      <td>2021-10-28</td>\n",
       "      <td>VZ</td>\n",
       "      <td>51.494991</td>\n",
       "      <td>51.885476</td>\n",
       "      <td>51.368083</td>\n",
       "      <td>51.543800</td>\n",
       "      <td>18374700.0</td>\n",
       "      <td>45.074776</td>\n",
       "      <td>51.831031</td>\n",
       "      <td>52.499389</td>\n",
       "      <td>16.530001</td>\n",
       "      <td>75.498292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93668</th>\n",
       "      <td>2021-10-28</td>\n",
       "      <td>WBA</td>\n",
       "      <td>45.627879</td>\n",
       "      <td>45.889548</td>\n",
       "      <td>45.220831</td>\n",
       "      <td>45.647259</td>\n",
       "      <td>4843900.0</td>\n",
       "      <td>42.299346</td>\n",
       "      <td>46.605758</td>\n",
       "      <td>47.059735</td>\n",
       "      <td>16.530001</td>\n",
       "      <td>75.498292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93669</th>\n",
       "      <td>2021-10-28</td>\n",
       "      <td>WMT</td>\n",
       "      <td>146.642860</td>\n",
       "      <td>147.275537</td>\n",
       "      <td>146.020078</td>\n",
       "      <td>146.751602</td>\n",
       "      <td>4199500.0</td>\n",
       "      <td>55.484341</td>\n",
       "      <td>140.626488</td>\n",
       "      <td>143.343793</td>\n",
       "      <td>16.530001</td>\n",
       "      <td>75.498292</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>93670 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             date   tic        open        high         low       close  \\\n",
       "0      2008-12-31  AAPL    2.625211    2.679260    2.605973    2.606278   \n",
       "1      2008-12-31  AMGN   43.437676   44.281939   43.399647   43.924458   \n",
       "2      2008-12-31   AXP   14.442319   15.069198   14.394098   14.908460   \n",
       "3      2008-12-31    BA   31.195798   32.290913   31.128291   32.005882   \n",
       "4      2008-12-31   CAT   29.963740   30.923676   29.963740   30.628838   \n",
       "...           ...   ...         ...         ...         ...         ...   \n",
       "93665  2021-10-28   UNH  448.595319  453.447669  448.119976  451.011597   \n",
       "93666  2021-10-28     V  218.092662  218.828761  207.906710  208.732330   \n",
       "93667  2021-10-28    VZ   51.494991   51.885476   51.368083   51.543800   \n",
       "93668  2021-10-28   WBA   45.627879   45.889548   45.220831   45.647259   \n",
       "93669  2021-10-28   WMT  146.642860  147.275537  146.020078  146.751602   \n",
       "\n",
       "            volume     rsi_30       ma_30       ma_60        vix  turbulence  \n",
       "0      607541200.0  59.908163    2.780335    2.841795  40.000000    0.000000  \n",
       "1        6287200.0  59.908163    2.780335    2.841795  40.000000    0.000000  \n",
       "2        9625600.0  59.908163    2.780335    2.841795  40.000000    0.000000  \n",
       "3        5443100.0  59.908163    2.780335    2.841795  40.000000    0.000000  \n",
       "4        6277400.0  59.908163    2.780335    2.841795  40.000000    0.000000  \n",
       "...            ...        ...         ...         ...        ...         ...  \n",
       "93665    1672600.0  64.621205  412.388166  411.987484  16.530001   75.498292  \n",
       "93666   23199400.0  42.595810  225.210895  227.487207  16.530001   75.498292  \n",
       "93667   18374700.0  45.074776   51.831031   52.499389  16.530001   75.498292  \n",
       "93668    4843900.0  42.299346   46.605758   47.059735  16.530001   75.498292  \n",
       "93669    4199500.0  55.484341  140.626488  143.343793  16.530001   75.498292  \n",
       "\n",
       "[93670 rows x 12 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import vectorbt as vbt\n",
    "import pandas as pd\n",
    "\n",
    "start_date = '2009-01-01'\n",
    "end_date = '2021-10-31'\n",
    "DOW_30_TICKER = ['AXP', 'AMGN', 'AAPL', 'BA', 'CAT', 'CSCO', 'CVX', 'GS', 'HD', 'HON', 'IBM', 'INTC', 'JNJ', 'KO', 'JPM', 'MCD', 'MMM', 'MRK', 'MSFT', 'NKE', 'PG', 'TRV', 'UNH', 'CRM', 'VZ', 'V', 'WBA', 'WMT', 'DIS', 'DOW']\n",
    "data_vectorbt = vbt.YFData.download(\n",
    "     DOW_30_TICKER,\n",
    "     start=start_date,\n",
    "     end=end_date,\n",
    "     interval='1D'\n",
    " )\n",
    "\n",
    "\n",
    "def preprocess_data(data):\n",
    "    '''convert vectorbt format data to finrl format, and add indicators'''\n",
    "    # dataframe schema is tuple with 7 elements: Open ,High ,Low ,Close ,Volume ,Dividends ,Stock Splits\n",
    "    open = data.get()[0].stack(dropna=False).rename('open')\n",
    "    high = data.get()[1].stack(dropna=False).rename('high')\n",
    "    low = data.get()[2].stack(dropna=False).rename('low')\n",
    "    close = data.get()[3].stack(dropna=False).rename('close')\n",
    "    volume = data.get()[4].stack(dropna=False).rename('volume')\n",
    "    close_rawFormat = data.get()[3]\n",
    "    \n",
    "    rsi_30 = vbt.RSI.run(close_rawFormat, 30).rsi.stack(dropna=False).rename(columns = {30 : 'rsi_30'})\n",
    "    ma_30 = vbt.MA.run(close_rawFormat, 30).ma.stack(dropna=False).rename(columns = {30 : 'ma_30'})\n",
    "    ma_60 = vbt.MA.run(close_rawFormat, 60).ma.stack(dropna=False).rename(columns = {60 : 'ma_60'})\n",
    "    res = pd.concat([open,high,low,close,volume,rsi_30,ma_30,ma_60],axis=1).reset_index() # is slow because this line\n",
    "    res = res.rename(columns = {'Date' : 'date', 'symbol' : 'tic'})\n",
    "    res['date'] = res['date'].apply(lambda x : x.strftime('%Y-%m-%d'))\n",
    "    fe = FeatureEngineer(\n",
    "                    use_technical_indicator=False,\n",
    "                    use_vix=True,\n",
    "                    use_turbulence=True,\n",
    "                    user_defined_feature = False)\n",
    "    processed = fe.preprocess_data(res)\n",
    "    return processed\n",
    "\n",
    "processed = preprocess_data(data_vectorbt)\n",
    "processed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>tic</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>rsi_30</th>\n",
       "      <th>ma_30</th>\n",
       "      <th>ma_60</th>\n",
       "      <th>vix</th>\n",
       "      <th>turbulence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2008-12-31</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>2.625210</td>\n",
       "      <td>2.679259</td>\n",
       "      <td>2.605973</td>\n",
       "      <td>2.606278</td>\n",
       "      <td>607541200.0</td>\n",
       "      <td>59.908196</td>\n",
       "      <td>2.780335</td>\n",
       "      <td>2.841794</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2008-12-31</td>\n",
       "      <td>AMGN</td>\n",
       "      <td>43.437665</td>\n",
       "      <td>44.281927</td>\n",
       "      <td>43.399636</td>\n",
       "      <td>43.924446</td>\n",
       "      <td>6287200.0</td>\n",
       "      <td>59.908196</td>\n",
       "      <td>2.780335</td>\n",
       "      <td>2.841794</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2008-12-31</td>\n",
       "      <td>AXP</td>\n",
       "      <td>14.442325</td>\n",
       "      <td>15.069204</td>\n",
       "      <td>14.394104</td>\n",
       "      <td>14.908465</td>\n",
       "      <td>9625600.0</td>\n",
       "      <td>59.908196</td>\n",
       "      <td>2.780335</td>\n",
       "      <td>2.841794</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2008-12-31</td>\n",
       "      <td>BA</td>\n",
       "      <td>31.195809</td>\n",
       "      <td>32.290925</td>\n",
       "      <td>31.128302</td>\n",
       "      <td>32.005894</td>\n",
       "      <td>5443100.0</td>\n",
       "      <td>59.908196</td>\n",
       "      <td>2.780335</td>\n",
       "      <td>2.841794</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2008-12-31</td>\n",
       "      <td>CAT</td>\n",
       "      <td>29.963731</td>\n",
       "      <td>30.923666</td>\n",
       "      <td>29.963731</td>\n",
       "      <td>30.628828</td>\n",
       "      <td>6277400.0</td>\n",
       "      <td>59.908196</td>\n",
       "      <td>2.780335</td>\n",
       "      <td>2.841794</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135860</th>\n",
       "      <td>2021-10-28</td>\n",
       "      <td>UNH</td>\n",
       "      <td>448.595289</td>\n",
       "      <td>453.447638</td>\n",
       "      <td>448.119945</td>\n",
       "      <td>451.011566</td>\n",
       "      <td>1672600.0</td>\n",
       "      <td>64.621232</td>\n",
       "      <td>412.388161</td>\n",
       "      <td>411.987482</td>\n",
       "      <td>16.530001</td>\n",
       "      <td>75.49797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135861</th>\n",
       "      <td>2021-10-28</td>\n",
       "      <td>V</td>\n",
       "      <td>218.092662</td>\n",
       "      <td>218.828761</td>\n",
       "      <td>207.906710</td>\n",
       "      <td>208.732330</td>\n",
       "      <td>23199400.0</td>\n",
       "      <td>42.595797</td>\n",
       "      <td>225.210895</td>\n",
       "      <td>227.487208</td>\n",
       "      <td>16.530001</td>\n",
       "      <td>75.49797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135862</th>\n",
       "      <td>2021-10-28</td>\n",
       "      <td>VZ</td>\n",
       "      <td>51.494994</td>\n",
       "      <td>51.885479</td>\n",
       "      <td>51.368086</td>\n",
       "      <td>51.543804</td>\n",
       "      <td>18374700.0</td>\n",
       "      <td>45.074834</td>\n",
       "      <td>51.831032</td>\n",
       "      <td>52.499389</td>\n",
       "      <td>16.530001</td>\n",
       "      <td>75.49797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135863</th>\n",
       "      <td>2021-10-28</td>\n",
       "      <td>WBA</td>\n",
       "      <td>45.627879</td>\n",
       "      <td>45.889548</td>\n",
       "      <td>45.220831</td>\n",
       "      <td>45.647259</td>\n",
       "      <td>4843900.0</td>\n",
       "      <td>42.299353</td>\n",
       "      <td>46.605758</td>\n",
       "      <td>47.059735</td>\n",
       "      <td>16.530001</td>\n",
       "      <td>75.49797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135864</th>\n",
       "      <td>2021-10-28</td>\n",
       "      <td>WMT</td>\n",
       "      <td>146.642845</td>\n",
       "      <td>147.275522</td>\n",
       "      <td>146.020063</td>\n",
       "      <td>146.751587</td>\n",
       "      <td>4199500.0</td>\n",
       "      <td>55.484286</td>\n",
       "      <td>140.626491</td>\n",
       "      <td>143.343797</td>\n",
       "      <td>16.530001</td>\n",
       "      <td>75.49797</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>93670 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              date   tic        open        high         low       close  \\\n",
       "0       2008-12-31  AAPL    2.625210    2.679259    2.605973    2.606278   \n",
       "1       2008-12-31  AMGN   43.437665   44.281927   43.399636   43.924446   \n",
       "2       2008-12-31   AXP   14.442325   15.069204   14.394104   14.908465   \n",
       "3       2008-12-31    BA   31.195809   32.290925   31.128302   32.005894   \n",
       "4       2008-12-31   CAT   29.963731   30.923666   29.963731   30.628828   \n",
       "...            ...   ...         ...         ...         ...         ...   \n",
       "135860  2021-10-28   UNH  448.595289  453.447638  448.119945  451.011566   \n",
       "135861  2021-10-28     V  218.092662  218.828761  207.906710  208.732330   \n",
       "135862  2021-10-28    VZ   51.494994   51.885479   51.368086   51.543804   \n",
       "135863  2021-10-28   WBA   45.627879   45.889548   45.220831   45.647259   \n",
       "135864  2021-10-28   WMT  146.642845  147.275522  146.020063  146.751587   \n",
       "\n",
       "             volume     rsi_30       ma_30       ma_60        vix  turbulence  \n",
       "0       607541200.0  59.908196    2.780335    2.841794  40.000000     0.00000  \n",
       "1         6287200.0  59.908196    2.780335    2.841794  40.000000     0.00000  \n",
       "2         9625600.0  59.908196    2.780335    2.841794  40.000000     0.00000  \n",
       "3         5443100.0  59.908196    2.780335    2.841794  40.000000     0.00000  \n",
       "4         6277400.0  59.908196    2.780335    2.841794  40.000000     0.00000  \n",
       "...             ...        ...         ...         ...        ...         ...  \n",
       "135860    1672600.0  64.621232  412.388161  411.987482  16.530001    75.49797  \n",
       "135861   23199400.0  42.595797  225.210895  227.487208  16.530001    75.49797  \n",
       "135862   18374700.0  45.074834   51.831032   52.499389  16.530001    75.49797  \n",
       "135863    4843900.0  42.299353   46.605758   47.059735  16.530001    75.49797  \n",
       "135864    4199500.0  55.484286  140.626491  143.343797  16.530001    75.49797  \n",
       "\n",
       "[93670 rows x 12 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list_ticker = processed[\"tic\"].unique().tolist()\n",
    "list_date = list(pd.date_range(processed['date'].min(),processed['date'].max()).astype(str))\n",
    "combination = list(itertools.product(list_date,list_ticker))\n",
    "\n",
    "processed_full = pd.DataFrame(combination,columns=[\"date\",\"tic\"]).merge(processed,on=[\"date\",\"tic\"],how=\"left\")\n",
    "processed_full = processed_full[processed_full['date'].isin(processed['date'])]\n",
    "processed_full = processed_full.sort_values(['date','tic'])\n",
    "\n",
    "processed_full = processed_full.fillna(0)\n",
    "processed_full"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-QsYaY0Dh1iw"
   },
   "source": [
    "<a id='4'></a>\n",
    "# Part 5. Design Environment\n",
    "Considering the stochastic and interactive nature of the automated stock trading tasks, a financial task is modeled as a **Markov Decision Process (MDP)** problem. The training process involves observing stock price change, taking an action and reward's calculation to have the agent adjusting its strategy accordingly. By interacting with the environment, the trading agent will derive a trading strategy with the maximized rewards as time proceeds.\n",
    "\n",
    "Our trading environments, based on OpenAI Gym framework, simulate live stock markets with real market data according to the principle of time-driven simulation.\n",
    "\n",
    "The action space describes the allowed actions that the agent interacts with the environment. Normally, action a includes three actions: {-1, 0, 1}, where -1, 0, 1 represent selling, holding, and buying one share. Also, an action can be carried upon multiple shares. We use an action space {-k,…,-1, 0, 1, …, k}, where k denotes the number of shares to buy and -k denotes the number of shares to sell. For example, \"Buy 10 shares of AAPL\" or \"Sell 10 shares of AAPL\" are 10 or -10, respectively. The continuous action space needs to be normalized to [-1, 1], since the policy is defined on a Gaussian distribution, which needs to be normalized and symmetric."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5TOhcryx44bb"
   },
   "source": [
    "## Training data split: 2009-01-01 to 2020-07-01\n",
    "## Trade data split: 2020-07-01 to 2021-10-31"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "W0qaVGjLtgbI",
    "outputId": "ca8d1a43-ffc3-4fc3-efa9-4de9a9065842"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "83897\n",
      "9744\n"
     ]
    }
   ],
   "source": [
    "train = data_split(processed_full, '2009-01-01','2020-07-01')\n",
    "trade = data_split(processed_full, '2020-07-01','2021-10-31')\n",
    "print(len(train))\n",
    "print(len(trade))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 357
    },
    "id": "p52zNCOhTtLR",
    "outputId": "b3ad3e10-376f-4186-f875-0331708c5e14"
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>tic</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>rsi_30</th>\n",
       "      <th>ma_30</th>\n",
       "      <th>ma_60</th>\n",
       "      <th>vix</th>\n",
       "      <th>turbulence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2892</th>\n",
       "      <td>2020-06-30</td>\n",
       "      <td>UNH</td>\n",
       "      <td>280.551458</td>\n",
       "      <td>288.212500</td>\n",
       "      <td>279.666741</td>\n",
       "      <td>286.754181</td>\n",
       "      <td>2932900.0</td>\n",
       "      <td>51.124605</td>\n",
       "      <td>286.997208</td>\n",
       "      <td>280.002903</td>\n",
       "      <td>30.43</td>\n",
       "      <td>12.91886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2892</th>\n",
       "      <td>2020-06-30</td>\n",
       "      <td>V</td>\n",
       "      <td>189.078409</td>\n",
       "      <td>191.309941</td>\n",
       "      <td>187.765157</td>\n",
       "      <td>190.737244</td>\n",
       "      <td>9040100.0</td>\n",
       "      <td>51.233293</td>\n",
       "      <td>191.485033</td>\n",
       "      <td>181.677680</td>\n",
       "      <td>30.43</td>\n",
       "      <td>12.91886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2892</th>\n",
       "      <td>2020-06-30</td>\n",
       "      <td>VZ</td>\n",
       "      <td>50.184839</td>\n",
       "      <td>50.522940</td>\n",
       "      <td>49.673124</td>\n",
       "      <td>50.376736</td>\n",
       "      <td>17414800.0</td>\n",
       "      <td>48.465911</td>\n",
       "      <td>51.012123</td>\n",
       "      <td>51.464679</td>\n",
       "      <td>30.43</td>\n",
       "      <td>12.91886</td>\n",
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       "    <tr>\n",
       "      <th>2892</th>\n",
       "      <td>2020-06-30</td>\n",
       "      <td>WBA</td>\n",
       "      <td>38.787097</td>\n",
       "      <td>39.210700</td>\n",
       "      <td>38.455582</td>\n",
       "      <td>39.035732</td>\n",
       "      <td>4782100.0</td>\n",
       "      <td>53.660862</td>\n",
       "      <td>39.135190</td>\n",
       "      <td>38.935130</td>\n",
       "      <td>30.43</td>\n",
       "      <td>12.91886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2892</th>\n",
       "      <td>2020-06-30</td>\n",
       "      <td>WMT</td>\n",
       "      <td>115.578878</td>\n",
       "      <td>116.461082</td>\n",
       "      <td>114.919646</td>\n",
       "      <td>116.121773</td>\n",
       "      <td>6836400.0</td>\n",
       "      <td>35.064430</td>\n",
       "      <td>117.787627</td>\n",
       "      <td>119.723274</td>\n",
       "      <td>30.43</td>\n",
       "      <td>12.91886</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            date  tic        open        high         low       close  \\\n",
       "2892  2020-06-30  UNH  280.551458  288.212500  279.666741  286.754181   \n",
       "2892  2020-06-30    V  189.078409  191.309941  187.765157  190.737244   \n",
       "2892  2020-06-30   VZ   50.184839   50.522940   49.673124   50.376736   \n",
       "2892  2020-06-30  WBA   38.787097   39.210700   38.455582   39.035732   \n",
       "2892  2020-06-30  WMT  115.578878  116.461082  114.919646  116.121773   \n",
       "\n",
       "          volume     rsi_30       ma_30       ma_60    vix  turbulence  \n",
       "2892   2932900.0  51.124605  286.997208  280.002903  30.43    12.91886  \n",
       "2892   9040100.0  51.233293  191.485033  181.677680  30.43    12.91886  \n",
       "2892  17414800.0  48.465911   51.012123   51.464679  30.43    12.91886  \n",
       "2892   4782100.0  53.660862   39.135190   38.935130  30.43    12.91886  \n",
       "2892   6836400.0  35.064430  117.787627  119.723274  30.43    12.91886  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 357
    },
    "id": "k9zU9YaTTvFq",
    "outputId": "72213585-39a3-4bff-c031-874ec0ca06f9"
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>tic</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>rsi_30</th>\n",
       "      <th>ma_30</th>\n",
       "      <th>ma_60</th>\n",
       "      <th>vix</th>\n",
       "      <th>turbulence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>90.153997</td>\n",
       "      <td>90.707087</td>\n",
       "      <td>89.855231</td>\n",
       "      <td>89.904610</td>\n",
       "      <td>110737200.0</td>\n",
       "      <td>67.961186</td>\n",
       "      <td>83.933768</td>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>AMGN</td>\n",
       "      <td>221.703759</td>\n",
       "      <td>241.198860</td>\n",
       "      <td>218.936225</td>\n",
       "      <td>240.153961</td>\n",
       "      <td>6575800.0</td>\n",
       "      <td>63.126952</td>\n",
       "      <td>214.858666</td>\n",
       "      <td>215.931668</td>\n",
       "      <td>28.620001</td>\n",
       "      <td>53.06816</td>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>AXP</td>\n",
       "      <td>93.261323</td>\n",
       "      <td>94.935620</td>\n",
       "      <td>91.684937</td>\n",
       "      <td>92.086380</td>\n",
       "      <td>3301000.0</td>\n",
       "      <td>54.344115</td>\n",
       "      <td>97.244636</td>\n",
       "      <td>90.695523</td>\n",
       "      <td>28.620001</td>\n",
       "      <td>53.06816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>BA</td>\n",
       "      <td>185.880005</td>\n",
       "      <td>190.610001</td>\n",
       "      <td>180.039993</td>\n",
       "      <td>180.320007</td>\n",
       "      <td>49036700.0</td>\n",
       "      <td>59.284491</td>\n",
       "      <td>176.472335</td>\n",
       "      <td>155.614168</td>\n",
       "      <td>28.620001</td>\n",
       "      <td>53.06816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>CAT</td>\n",
       "      <td>123.829211</td>\n",
       "      <td>123.848342</td>\n",
       "      <td>120.479364</td>\n",
       "      <td>120.651642</td>\n",
       "      <td>2807800.0</td>\n",
       "      <td>58.750622</td>\n",
       "      <td>119.412836</td>\n",
       "      <td>113.646675</td>\n",
       "      <td>28.620001</td>\n",
       "      <td>53.06816</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date   tic        open        high         low       close  \\\n",
       "0  2020-07-01  AAPL   90.153997   90.707087   89.855231   89.904610   \n",
       "0  2020-07-01  AMGN  221.703759  241.198860  218.936225  240.153961   \n",
       "0  2020-07-01   AXP   93.261323   94.935620   91.684937   92.086380   \n",
       "0  2020-07-01    BA  185.880005  190.610001  180.039993  180.320007   \n",
       "0  2020-07-01   CAT  123.829211  123.848342  120.479364  120.651642   \n",
       "\n",
       "        volume     rsi_30       ma_30       ma_60        vix  turbulence  \n",
       "0  110737200.0  67.961186   83.933768   77.717542  28.620001    53.06816  \n",
       "0    6575800.0  63.126952  214.858666  215.931668  28.620001    53.06816  \n",
       "0    3301000.0  54.344115   97.244636   90.695523  28.620001    53.06816  \n",
       "0   49036700.0  59.284491  176.472335  155.614168  28.620001    53.06816  \n",
       "0    2807800.0  58.750622  119.412836  113.646675  28.620001    53.06816  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trade.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "zYN573SOHhxG",
    "outputId": "7f228183-abe3-4477-f574-3c9b25c62cd8",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "INDICATORS = ['rsi_30', 'ma_30','ma_60']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Q2zqII8rMIqn",
    "outputId": "1f54d044-e2d3-4a34-c041-e913d686654e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stock Dimension: 29, State Space: 146\n"
     ]
    }
   ],
   "source": [
    "stock_dimension = len(train.tic.unique())\n",
    "state_space = 1 + 2*stock_dimension + len(INDICATORS)*stock_dimension\n",
    "print(f\"Stock Dimension: {stock_dimension}, State Space: {state_space}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "id": "AWyp84Ltto19"
   },
   "outputs": [],
   "source": [
    "buy_cost_list = sell_cost_list = [0.001] * stock_dimension\n",
    "num_stock_shares = [0] * stock_dimension\n",
    "\n",
    "env_kwargs = {\n",
    "    \"hmax\": 100,\n",
    "    \"initial_amount\": 1000000,\n",
    "    \"num_stock_shares\": num_stock_shares,\n",
    "    \"buy_cost_pct\": buy_cost_list,\n",
    "    \"sell_cost_pct\": sell_cost_list,\n",
    "    \"state_space\": state_space,\n",
    "    \"stock_dim\": stock_dimension,\n",
    "    \"tech_indicator_list\": INDICATORS,\n",
    "    \"action_space\": stock_dimension,\n",
    "    \"reward_scaling\": 1e-4\n",
    "}\n",
    "e_train_gym = StockTradingEnv(df = train, **env_kwargs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "64EoqOrQjiVf"
   },
   "source": [
    "## Environment for Training\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "xwSvvPjutpqS",
    "outputId": "deeaef07-afda-4ca1-fea8-99384224c7cf"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'stable_baselines3.common.vec_env.dummy_vec_env.DummyVecEnv'>\n"
     ]
    }
   ],
   "source": [
    "env_train, _ = e_train_gym.get_sb_env()\n",
    "print(type(env_train))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HMNR5nHjh1iz"
   },
   "source": [
    "<a id='5'></a>\n",
    "# Part 6: Implement DRL Algorithms\n",
    "* The implementation of the DRL algorithms are based on **OpenAI Baselines** and **Stable Baselines**. Stable Baselines is a fork of OpenAI Baselines, with a major structural refactoring, and code cleanups.\n",
    "* FinRL library includes fine-tuned standard DRL algorithms, such as DQN, DDPG,\n",
    "Multi-Agent DDPG, PPO, SAC, A2C and TD3. We also allow users to\n",
    "design their own DRL algorithms by adapting these DRL algorithms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "id": "364PsqckttcQ"
   },
   "outputs": [],
   "source": [
    "agent = DRLAgent(env = env_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YDmqOyF9h1iz"
   },
   "source": [
    "### Model Training: 5 models, A2C DDPG, PPO, TD3, SAC\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uijiWgkuh1jB"
   },
   "source": [
    "### Model 1: A2C\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "GUCnkn-HIbmj",
    "outputId": "a90a7a60-21a5-47e1-b683-1f7cbb4b8bc0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'n_steps': 5, 'ent_coef': 0.01, 'learning_rate': 0.0007}\n",
      "Using cpu device\n"
     ]
    }
   ],
   "source": [
    "agent = DRLAgent(env = env_train)\n",
    "model_a2c = agent.get_model(\"a2c\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "0GVpkWGqH4-D",
    "outputId": "570d540f-abe9-402b-e0cc-f9b007228e8e"
   },
   "outputs": [],
   "source": [
    "trained_a2c = agent.train_model(model=model_a2c, \n",
    "                             tb_log_name='a2c',\n",
    "                             total_timesteps=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MRiOtrywfAo1"
   },
   "source": [
    "### Model 2: DDPG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "id": "M2YadjfnLwgt"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'batch_size': 128, 'buffer_size': 50000, 'learning_rate': 0.001}\n",
      "Using cpu device\n"
     ]
    }
   ],
   "source": [
    "agent = DRLAgent(env = env_train)\n",
    "model_ddpg = agent.get_model(\"ddpg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "id": "tCDa78rqfO_a",
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-39-87b09b7794a1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m trained_ddpg = agent.train_model(model=model_ddpg, \n\u001b[0m\u001b[1;32m      2\u001b[0m                              \u001b[0mtb_log_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'ddpg'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m                              total_timesteps=50000)\n",
      "\u001b[0;32m/mnt/recoverData/linuxProgram/workspace/Finrl_python3.9/finrl/agents/stablebaselines3/models.py\u001b[0m in \u001b[0;36mtrain_model\u001b[0;34m(self, model, tb_log_name, total_timesteps)\u001b[0m\n\u001b[1;32m     98\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     99\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mtrain_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtb_log_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtotal_timesteps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m5000\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 100\u001b[0;31m         model = model.learn(\n\u001b[0m\u001b[1;32m    101\u001b[0m             \u001b[0mtotal_timesteps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtotal_timesteps\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    102\u001b[0m             \u001b[0mtb_log_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtb_log_name\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/mnt/recoverData/linuxProgram/workspace/Finrl_python3.9/__pypackages__/3.9/lib/stable_baselines3/ddpg/ddpg.py\u001b[0m in \u001b[0;36mlearn\u001b[0;34m(self, total_timesteps, callback, log_interval, eval_env, eval_freq, n_eval_episodes, tb_log_name, eval_log_path, reset_num_timesteps)\u001b[0m\n\u001b[1;32m    128\u001b[0m     ) -> OffPolicyAlgorithm:\n\u001b[1;32m    129\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 130\u001b[0;31m         return super(DDPG, self).learn(\n\u001b[0m\u001b[1;32m    131\u001b[0m             \u001b[0mtotal_timesteps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtotal_timesteps\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    132\u001b[0m             \u001b[0mcallback\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcallback\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/mnt/recoverData/linuxProgram/workspace/Finrl_python3.9/__pypackages__/3.9/lib/stable_baselines3/td3/td3.py\u001b[0m in \u001b[0;36mlearn\u001b[0;34m(self, total_timesteps, callback, log_interval, eval_env, eval_freq, n_eval_episodes, tb_log_name, eval_log_path, reset_num_timesteps)\u001b[0m\n\u001b[1;32m    203\u001b[0m     ) -> OffPolicyAlgorithm:\n\u001b[1;32m    204\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 205\u001b[0;31m         return super(TD3, self).learn(\n\u001b[0m\u001b[1;32m    206\u001b[0m             \u001b[0mtotal_timesteps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtotal_timesteps\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    207\u001b[0m             \u001b[0mcallback\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcallback\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/mnt/recoverData/linuxProgram/workspace/Finrl_python3.9/__pypackages__/3.9/lib/stable_baselines3/common/off_policy_algorithm.py\u001b[0m in \u001b[0;36mlearn\u001b[0;34m(self, total_timesteps, callback, log_interval, eval_env, eval_freq, n_eval_episodes, tb_log_name, eval_log_path, reset_num_timesteps)\u001b[0m\n\u001b[1;32m    345\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    346\u001b[0m         \u001b[0;32mwhile\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_timesteps\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0mtotal_timesteps\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 347\u001b[0;31m             rollout = self.collect_rollouts(\n\u001b[0m\u001b[1;32m    348\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0menv\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    349\u001b[0m                 \u001b[0mtrain_freq\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_freq\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/mnt/recoverData/linuxProgram/workspace/Finrl_python3.9/__pypackages__/3.9/lib/stable_baselines3/common/off_policy_algorithm.py\u001b[0m in \u001b[0;36mcollect_rollouts\u001b[0;34m(self, env, callback, train_freq, replay_buffer, action_noise, learning_starts, log_interval)\u001b[0m\n\u001b[1;32m    578\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    579\u001b[0m             \u001b[0;31m# Rescale and perform action\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 580\u001b[0;31m             \u001b[0mnew_obs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrewards\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdones\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minfos\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0menv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mactions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    581\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    582\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_timesteps\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0menv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_envs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/mnt/recoverData/linuxProgram/workspace/Finrl_python3.9/__pypackages__/3.9/lib/stable_baselines3/common/vec_env/base_vec_env.py\u001b[0m in \u001b[0;36mstep\u001b[0;34m(self, actions)\u001b[0m\n\u001b[1;32m    160\u001b[0m         \"\"\"\n\u001b[1;32m    161\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep_async\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mactions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 162\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep_wait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    163\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    164\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget_images\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mSequence\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/mnt/recoverData/linuxProgram/workspace/Finrl_python3.9/__pypackages__/3.9/lib/stable_baselines3/common/vec_env/dummy_vec_env.py\u001b[0m in \u001b[0;36mstep_wait\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     41\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mstep_wait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mVecEnvStepReturn\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     42\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0menv_idx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_envs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 43\u001b[0;31m             obs, self.buf_rews[env_idx], self.buf_dones[env_idx], self.buf_infos[env_idx] = self.envs[env_idx].step(\n\u001b[0m\u001b[1;32m     44\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mactions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0menv_idx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     45\u001b[0m             )\n",
      "\u001b[0;32m/mnt/recoverData/linuxProgram/workspace/Finrl_python3.9/finrl/finrl_meta/env_stock_trading/env_stocktrading.py\u001b[0m in \u001b[0;36mstep\u001b[0;34m(self, actions)\u001b[0m\n\u001b[1;32m    321\u001b[0m             )\n\u001b[1;32m    322\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masset_memory\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mend_total_asset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 323\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdate_memory\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_date\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    324\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreward\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mend_total_asset\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mbegin_total_asset\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    325\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrewards_memory\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreward\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/mnt/recoverData/linuxProgram/workspace/Finrl_python3.9/finrl/finrl_meta/env_stock_trading/env_stocktrading.py\u001b[0m in \u001b[0;36m_get_date\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    445\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    446\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_get_date\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 447\u001b[0;31m         \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtic\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    448\u001b[0m             \u001b[0mdate\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdate\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    449\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/mnt/recoverData/linuxProgram/workspace/Finrl_python3.9/__pypackages__/3.9/lib/pandas/core/series.py\u001b[0m in \u001b[0;36munique\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   2086\u001b[0m         \u001b[0mCategories\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'a'\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;34m'b'\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;34m'c'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2087\u001b[0m         \"\"\"\n\u001b[0;32m-> 2088\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2089\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2090\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0moverload\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/mnt/recoverData/linuxProgram/workspace/Finrl_python3.9/__pypackages__/3.9/lib/pandas/core/base.py\u001b[0m in \u001b[0;36munique\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    987\u001b[0m                     \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    988\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 989\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0munique1d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    990\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    991\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/mnt/recoverData/linuxProgram/workspace/Finrl_python3.9/__pypackages__/3.9/lib/pandas/core/algorithms.py\u001b[0m in \u001b[0;36munique\u001b[0;34m(values)\u001b[0m\n\u001b[1;32m    435\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    436\u001b[0m     \u001b[0moriginal\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 437\u001b[0;31m     \u001b[0mhtable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_get_hashtable_algo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    438\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    439\u001b[0m     \u001b[0mtable\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhtable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/mnt/recoverData/linuxProgram/workspace/Finrl_python3.9/__pypackages__/3.9/lib/pandas/core/algorithms.py\u001b[0m in \u001b[0;36m_get_hashtable_algo\u001b[0;34m(values)\u001b[0m\n\u001b[1;32m    282\u001b[0m     \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_ensure_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    283\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 284\u001b[0;31m     \u001b[0mndtype\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_check_object_for_strings\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    285\u001b[0m     \u001b[0mhtable\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_hashtables\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mndtype\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    286\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mhtable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/mnt/recoverData/linuxProgram/workspace/Finrl_python3.9/__pypackages__/3.9/lib/pandas/core/algorithms.py\u001b[0m in \u001b[0;36m_check_object_for_strings\u001b[0;34m(values)\u001b[0m\n\u001b[1;32m    324\u001b[0m         \u001b[0;31m# including nulls because that is the only difference between\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    325\u001b[0m         \u001b[0;31m# StringHashTable and ObjectHashtable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 326\u001b[0;31m         \u001b[0;32mif\u001b[0m \u001b[0mlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minfer_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mskipna\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\"string\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    327\u001b[0m             \u001b[0mndtype\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"string\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    328\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mndtype\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "trained_ddpg = agent.train_model(model=model_ddpg, \n",
    "                             tb_log_name='ddpg',\n",
    "                             total_timesteps=50000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_gDkU-j-fCmZ"
   },
   "source": [
    "### Model 3: PPO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "y5D5PFUhMzSV"
   },
   "outputs": [],
   "source": [
    "agent = DRLAgent(env = env_train)\n",
    "PPO_PARAMS = {\n",
    "    \"n_steps\": 2048,\n",
    "    \"ent_coef\": 0.01,\n",
    "    \"learning_rate\": 0.00025,\n",
    "    \"batch_size\": 128,\n",
    "}\n",
    "model_ppo = agent.get_model(\"ppo\",model_kwargs = PPO_PARAMS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Gt8eIQKYM4G3",
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "trained_ppo = agent.train_model(model=model_ppo, \n",
    "                             tb_log_name='ppo',\n",
    "                             total_timesteps=50000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3Zpv4S0-fDBv"
   },
   "source": [
    "### Model 4: TD3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "JSAHhV4Xc-bh"
   },
   "outputs": [],
   "source": [
    "agent = DRLAgent(env = env_train)\n",
    "TD3_PARAMS = {\"batch_size\": 100, \n",
    "              \"buffer_size\": 1000000, \n",
    "              \"learning_rate\": 0.001}\n",
    "\n",
    "model_td3 = agent.get_model(\"td3\",model_kwargs = TD3_PARAMS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "OSRxNYAxdKpU"
   },
   "outputs": [],
   "source": [
    "trained_td3 = agent.train_model(model=model_td3, \n",
    "                             tb_log_name='td3',\n",
    "                             total_timesteps=30000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Dr49PotrfG01"
   },
   "source": [
    "### Model 5: SAC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "xwOhVjqRkCdM"
   },
   "outputs": [],
   "source": [
    "agent = DRLAgent(env = env_train)\n",
    "SAC_PARAMS = {\n",
    "    \"batch_size\": 128,\n",
    "    \"buffer_size\": 1000000,\n",
    "    \"learning_rate\": 0.0001,\n",
    "    \"learning_starts\": 100,\n",
    "    \"ent_coef\": \"auto_0.1\",\n",
    "}\n",
    "\n",
    "model_sac = agent.get_model(\"sac\",model_kwargs = SAC_PARAMS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "K8RSdKCckJyH"
   },
   "outputs": [],
   "source": [
    "trained_sac = agent.train_model(model=model_sac, \n",
    "                             tb_log_name='sac',\n",
    "                             total_timesteps=60000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "f2wZgkQXh1jE"
   },
   "source": [
    "## Trading\n",
    "Assume that we have $1,000,000 initial capital at 2020-07-01. We use the DDPG model to trade Dow jones 30 stocks."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bEv5KGC8h1jE"
   },
   "source": [
    "### Set turbulence threshold\n",
    "Set the turbulence threshold to be greater than the maximum of insample turbulence data, if current turbulence index is greater than the threshold, then we assume that the current market is volatile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "id": "efwBi84ch1jE"
   },
   "outputs": [],
   "source": [
    "data_risk_indicator = processed_full[(processed_full.date<'2020-07-01') & (processed_full.date>='2009-01-01')]\n",
    "insample_risk_indicator = data_risk_indicator.drop_duplicates(subset=['date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "id": "VHZMBpSqh1jG"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    2893.000000\n",
       "mean       18.824245\n",
       "std         8.489311\n",
       "min         9.140000\n",
       "25%        13.330000\n",
       "50%        16.139999\n",
       "75%        21.309999\n",
       "max        82.690002\n",
       "Name: vix, dtype: float64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "insample_risk_indicator.vix.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "id": "BDkszkMloRWT"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "57.40400183105453"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "insample_risk_indicator.vix.quantile(0.996)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "id": "AL7hs7svnNWT"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    2893.000000\n",
       "mean       34.574234\n",
       "std        43.787196\n",
       "min         0.000000\n",
       "25%        14.966209\n",
       "50%        24.124394\n",
       "75%        39.162353\n",
       "max       652.506833\n",
       "Name: turbulence, dtype: float64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "insample_risk_indicator.turbulence.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "id": "N78hfHckoqJ9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "276.45144378730663"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "insample_risk_indicator.turbulence.quantile(0.996)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "U5mmgQF_h1jQ"
   },
   "source": [
    "### Trade\n",
    "\n",
    "DRL model needs to update periodically in order to take full advantage of the data, ideally we need to retrain our model yearly, quarterly, or monthly. We also need to tune the parameters along the way, in this notebook I only use the in-sample data from 2009-01 to 2020-07 to tune the parameters once, so there is some alpha decay here as the length of trade date extends. \n",
    "\n",
    "Numerous hyperparameters – e.g. the learning rate, the total number of samples to train on – influence the learning process and are usually determined by testing some variations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "id": "cIqoV0GSI52v"
   },
   "outputs": [],
   "source": [
    "#trade = data_split(processed_full, '2020-07-01','2021-10-31')\n",
    "e_trade_gym = StockTradingEnv(df = trade, turbulence_threshold = 70,risk_indicator_col='vix', **env_kwargs)\n",
    "# env_trade, obs_trade = e_trade_gym.get_sb_env()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "id": "W_XNgGsBMeVw"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>tic</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>rsi_30</th>\n",
       "      <th>ma_30</th>\n",
       "      <th>ma_60</th>\n",
       "      <th>vix</th>\n",
       "      <th>turbulence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>90.153997</td>\n",
       "      <td>90.707087</td>\n",
       "      <td>89.855231</td>\n",
       "      <td>89.904610</td>\n",
       "      <td>110737200.0</td>\n",
       "      <td>67.961186</td>\n",
       "      <td>83.933768</td>\n",
       "      <td>77.717542</td>\n",
       "      <td>28.620001</td>\n",
       "      <td>53.06816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>AMGN</td>\n",
       "      <td>221.703759</td>\n",
       "      <td>241.198860</td>\n",
       "      <td>218.936225</td>\n",
       "      <td>240.153961</td>\n",
       "      <td>6575800.0</td>\n",
       "      <td>63.126952</td>\n",
       "      <td>214.858666</td>\n",
       "      <td>215.931668</td>\n",
       "      <td>28.620001</td>\n",
       "      <td>53.06816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>AXP</td>\n",
       "      <td>93.261323</td>\n",
       "      <td>94.935620</td>\n",
       "      <td>91.684937</td>\n",
       "      <td>92.086380</td>\n",
       "      <td>3301000.0</td>\n",
       "      <td>54.344115</td>\n",
       "      <td>97.244636</td>\n",
       "      <td>90.695523</td>\n",
       "      <td>28.620001</td>\n",
       "      <td>53.06816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>BA</td>\n",
       "      <td>185.880005</td>\n",
       "      <td>190.610001</td>\n",
       "      <td>180.039993</td>\n",
       "      <td>180.320007</td>\n",
       "      <td>49036700.0</td>\n",
       "      <td>59.284491</td>\n",
       "      <td>176.472335</td>\n",
       "      <td>155.614168</td>\n",
       "      <td>28.620001</td>\n",
       "      <td>53.06816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>CAT</td>\n",
       "      <td>123.829211</td>\n",
       "      <td>123.848342</td>\n",
       "      <td>120.479364</td>\n",
       "      <td>120.651642</td>\n",
       "      <td>2807800.0</td>\n",
       "      <td>58.750622</td>\n",
       "      <td>119.412836</td>\n",
       "      <td>113.646675</td>\n",
       "      <td>28.620001</td>\n",
       "      <td>53.06816</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date   tic        open        high         low       close  \\\n",
       "0  2020-07-01  AAPL   90.153997   90.707087   89.855231   89.904610   \n",
       "0  2020-07-01  AMGN  221.703759  241.198860  218.936225  240.153961   \n",
       "0  2020-07-01   AXP   93.261323   94.935620   91.684937   92.086380   \n",
       "0  2020-07-01    BA  185.880005  190.610001  180.039993  180.320007   \n",
       "0  2020-07-01   CAT  123.829211  123.848342  120.479364  120.651642   \n",
       "\n",
       "        volume     rsi_30       ma_30       ma_60        vix  turbulence  \n",
       "0  110737200.0  67.961186   83.933768   77.717542  28.620001    53.06816  \n",
       "0    6575800.0  63.126952  214.858666  215.931668  28.620001    53.06816  \n",
       "0    3301000.0  54.344115   97.244636   90.695523  28.620001    53.06816  \n",
       "0   49036700.0  59.284491  176.472335  155.614168  28.620001    53.06816  \n",
       "0    2807800.0  58.750622  119.412836  113.646675  28.620001    53.06816  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trade.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "id": "eLOnL5eYh1jR"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hit end!\n"
     ]
    }
   ],
   "source": [
    "df_account_value, df_actions = DRLAgent.DRL_prediction(\n",
    "    model=trained_a2c, \n",
    "    environment = e_trade_gym)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "id": "ERxw3KqLkcP4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(336, 2)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_account_value.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "id": "2yRkNguY5yvp"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>account_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>331</th>\n",
       "      <td>2021-10-22</td>\n",
       "      <td>1.504965e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>332</th>\n",
       "      <td>2021-10-25</td>\n",
       "      <td>1.508471e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>333</th>\n",
       "      <td>2021-10-26</td>\n",
       "      <td>1.508891e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>334</th>\n",
       "      <td>2021-10-27</td>\n",
       "      <td>1.494264e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335</th>\n",
       "      <td>2021-10-28</td>\n",
       "      <td>1.495409e+06</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "           date  account_value\n",
       "331  2021-10-22   1.504965e+06\n",
       "332  2021-10-25   1.508471e+06\n",
       "333  2021-10-26   1.508891e+06\n",
       "334  2021-10-27   1.494264e+06\n",
       "335  2021-10-28   1.495409e+06"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_account_value.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "id": "nFlK5hNbWVFk"
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AAPL</th>\n",
       "      <th>AMGN</th>\n",
       "      <th>AXP</th>\n",
       "      <th>BA</th>\n",
       "      <th>CAT</th>\n",
       "      <th>CRM</th>\n",
       "      <th>CSCO</th>\n",
       "      <th>CVX</th>\n",
       "      <th>DIS</th>\n",
       "      <th>GS</th>\n",
       "      <th>...</th>\n",
       "      <th>MRK</th>\n",
       "      <th>MSFT</th>\n",
       "      <th>NKE</th>\n",
       "      <th>PG</th>\n",
       "      <th>TRV</th>\n",
       "      <th>UNH</th>\n",
       "      <th>V</th>\n",
       "      <th>VZ</th>\n",
       "      <th>WBA</th>\n",
       "      <th>WMT</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-07-01</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>...</td>\n",
       "      <td>31</td>\n",
       "      <td>43</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>100</td>\n",
       "      <td>83</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-02</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>...</td>\n",
       "      <td>31</td>\n",
       "      <td>43</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>100</td>\n",
       "      <td>83</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-06</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>...</td>\n",
       "      <td>31</td>\n",
       "      <td>43</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>100</td>\n",
       "      <td>83</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-07</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>...</td>\n",
       "      <td>31</td>\n",
       "      <td>43</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>100</td>\n",
       "      <td>83</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-08</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>...</td>\n",
       "      <td>31</td>\n",
       "      <td>43</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>100</td>\n",
       "      <td>83</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            AAPL  AMGN  AXP  BA  CAT  CRM  CSCO  CVX  DIS   GS  ...  MRK  \\\n",
       "date                                                            ...        \n",
       "2020-07-01     0     0  100  41    0    0     0    0    0  100  ...   31   \n",
       "2020-07-02     0     0  100  41    0    0     0    0    0  100  ...   31   \n",
       "2020-07-06     0     0  100  41    0    0     0    0    0  100  ...   31   \n",
       "2020-07-07     0     0  100  41    0    0     0    0    0  100  ...   31   \n",
       "2020-07-08     0     0  100  41    0    0     0    0    0  100  ...   31   \n",
       "\n",
       "            MSFT  NKE  PG  TRV  UNH   V   VZ  WBA  WMT  \n",
       "date                                                    \n",
       "2020-07-01    43    0   0    2  100  83  100  100  100  \n",
       "2020-07-02    43    0   0    2  100  83  100  100  100  \n",
       "2020-07-06    43    0   0    2  100  83  100  100  100  \n",
       "2020-07-07    43    0   0    2  100  83  100  100  100  \n",
       "2020-07-08    43    0   0    2  100  83  100  100  100  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_actions.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "W6vvNSC6h1jZ"
   },
   "source": [
    "<a id='6'></a>\n",
    "# Part 7: Backtest Our Strategy\n",
    "Backtesting plays a key role in evaluating the performance of a trading strategy. Automated backtesting tool is preferred because it reduces the human error. We usually use the Quantopian pyfolio package to backtest our trading strategies. It is easy to use and consists of various individual plots that provide a comprehensive image of the performance of a trading strategy."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Lr2zX7ZxNyFQ"
   },
   "source": [
    "<a id='6.1'></a>\n",
    "## 7.1 BackTestStats\n",
    "pass in df_account_value, this information is stored in env class\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "id": "Nzkr9yv-AdV_"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==============Get Backtest Results===========\n",
      "Annual return          0.352291\n",
      "Cumulative returns     0.495409\n",
      "Annual volatility      0.144451\n",
      "Sharpe ratio           2.168822\n",
      "Calmar ratio           3.946629\n",
      "Stability              0.944923\n",
      "Max drawdown          -0.089264\n",
      "Omega ratio            1.437034\n",
      "Sortino ratio          3.320179\n",
      "Skew                        NaN\n",
      "Kurtosis                    NaN\n",
      "Tail ratio             1.074948\n",
      "Daily value at risk   -0.016956\n",
      "dtype: float64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/mnt/recoverData/linuxProgram/workspace/Finrl_python3.9/__pypackages__/3.9/lib/pyfolio/timeseries.py:724: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "  stats = pd.Series()\n"
     ]
    }
   ],
   "source": [
    "print(\"==============Get Backtest Results===========\")\n",
    "now = datetime.datetime.now().strftime('%Y%m%d-%Hh%M')\n",
    "\n",
    "perf_stats_all = backtest_stats(account_value=df_account_value)\n",
    "perf_stats_all = pd.DataFrame(perf_stats_all)\n",
    "perf_stats_all.to_csv(\"./\"+config.RESULTS_DIR+\"/perf_stats_all_\"+now+'.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "id": "QkV-LB66iwhD"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==============Get Baseline Stats===========\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (336, 8)\n",
      "Annual return          0.269722\n",
      "Cumulative returns     0.374922\n",
      "Annual volatility      0.139083\n",
      "Sharpe ratio           1.792302\n",
      "Calmar ratio           3.020136\n",
      "Stability              0.919220\n",
      "Max drawdown          -0.089308\n",
      "Omega ratio            1.347571\n",
      "Sortino ratio          2.655481\n",
      "Skew                        NaN\n",
      "Kurtosis                    NaN\n",
      "Tail ratio             1.052781\n",
      "Daily value at risk   -0.016534\n",
      "dtype: float64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/mnt/recoverData/linuxProgram/workspace/Finrl_python3.9/finrl/finrl_meta/preprocessor/yahoodownloader.py:51: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data_df = data_df.append(temp_df)\n",
      "/mnt/recoverData/linuxProgram/workspace/Finrl_python3.9/__pypackages__/3.9/lib/pyfolio/timeseries.py:724: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "  stats = pd.Series()\n"
     ]
    }
   ],
   "source": [
    "#baseline stats\n",
    "print(\"==============Get Baseline Stats===========\")\n",
    "baseline_df = get_baseline(\n",
    "        ticker=\"^DJI\", \n",
    "        start = df_account_value.loc[0,'date'],\n",
    "        end = df_account_value.loc[len(df_account_value)-1,'date'])\n",
    "\n",
    "stats = backtest_stats(baseline_df, value_col_name = 'close')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "id": "qg1kvfemrrQH"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2020-07-01'"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_account_value.loc[0,'date']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "id": "tt1bzL5OrsTa"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2021-10-28'"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_account_value.loc[len(df_account_value)-1,'date']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9U6Suru3h1jc"
   },
   "source": [
    "<a id='6.2'></a>\n",
    "## 7.2 BackTestPlot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "id": "lKRGftSS7pNM"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==============Compare to DJIA===========\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (336, 8)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/mnt/recoverData/linuxProgram/workspace/Finrl_python3.9/finrl/finrl_meta/preprocessor/yahoodownloader.py:51: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data_df = data_df.append(temp_df)\n",
      "/mnt/recoverData/linuxProgram/workspace/Finrl_python3.9/__pypackages__/3.9/lib/pyfolio/timeseries.py:724: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "  stats = pd.Series()\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\"><th>Start date</th><td colspan=2>2020-07-01</td></tr>\n",
       "    <tr style=\"text-align: right;\"><th>End date</th><td colspan=2>2021-10-28</td></tr>\n",
       "    <tr style=\"text-align: right;\"><th>Total months</th><td colspan=2>16</td></tr>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Backtest</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Annual return</th>\n",
       "      <td>35.229%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cumulative returns</th>\n",
       "      <td>49.541%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Annual volatility</th>\n",
       "      <td>14.445%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sharpe ratio</th>\n",
       "      <td>2.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Calmar ratio</th>\n",
       "      <td>3.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stability</th>\n",
       "      <td>0.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Max drawdown</th>\n",
       "      <td>-8.926%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Omega ratio</th>\n",
       "      <td>1.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sortino ratio</th>\n",
       "      <td>3.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Skew</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kurtosis</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tail ratio</th>\n",
       "      <td>1.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Daily value at risk</th>\n",
       "      <td>-1.696%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alpha</th>\n",
       "      <td>0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Beta</th>\n",
       "      <td>0.98</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Worst drawdown periods</th>\n",
       "      <th>Net drawdown in %</th>\n",
       "      <th>Peak date</th>\n",
       "      <th>Valley date</th>\n",
       "      <th>Recovery date</th>\n",
       "      <th>Duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8.93</td>\n",
       "      <td>2020-09-02</td>\n",
       "      <td>2020-10-30</td>\n",
       "      <td>2020-11-09</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.89</td>\n",
       "      <td>2021-08-11</td>\n",
       "      <td>2021-09-30</td>\n",
       "      <td>2021-10-15</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.87</td>\n",
       "      <td>2021-01-12</td>\n",
       "      <td>2021-01-29</td>\n",
       "      <td>2021-02-17</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.69</td>\n",
       "      <td>2021-05-07</td>\n",
       "      <td>2021-06-18</td>\n",
       "      <td>2021-07-09</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.30</td>\n",
       "      <td>2020-11-16</td>\n",
       "      <td>2020-11-20</td>\n",
       "      <td>2020-12-02</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/mnt/recoverData/linuxProgram/workspace/Finrl_python3.9/__pypackages__/3.9/lib/pyfolio/plotting.py:805: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "  oos_cum_returns = pd.Series([])\n",
      "/mnt/recoverData/linuxProgram/workspace/Finrl_python3.9/__pypackages__/3.9/lib/pyfolio/plotting.py:805: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "  oos_cum_returns = pd.Series([])\n",
      "/mnt/recoverData/linuxProgram/workspace/Finrl_python3.9/__pypackages__/3.9/lib/pyfolio/plotting.py:805: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "  oos_cum_returns = pd.Series([])\n",
      "/mnt/recoverData/linuxProgram/workspace/Finrl_python3.9/__pypackages__/3.9/lib/pyfolio/timeseries.py:541: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "  out = pd.Series(index=returns.index)\n",
      "/mnt/recoverData/linuxProgram/workspace/Finrl_python3.9/__pypackages__/3.9/lib/pyfolio/timeseries.py:541: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "  out = pd.Series(index=returns.index)\n",
      "/mnt/recoverData/linuxProgram/workspace/Finrl_python3.9/__pypackages__/3.9/lib/pyfolio/timeseries.py:1230: FutureWarning: Indexing a timezone-aware DatetimeIndex with a timezone-naive datetime is deprecated and will raise KeyError in a future version. Use a timezone-aware object instead.\n",
      "  period = returns_dupe.loc[start:end]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Stress Events</th>\n",
       "      <th>mean</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>New Normal</th>\n",
       "      <td>0.12%</td>\n",
       "      <td>-3.29%</td>\n",
       "      <td>2.65%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/mnt/recoverData/linuxProgram/workspace/Finrl_python3.9/__pypackages__/3.9/lib/pyfolio/timeseries.py:1230: FutureWarning: Indexing a timezone-aware DatetimeIndex with a timezone-naive datetime is deprecated and will raise KeyError in a future version. Use a timezone-aware object instead.\n",
      "  period = returns_dupe.loc[start:end]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1008x5184 with 13 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"==============Compare to DJIA===========\")\n",
    "%matplotlib inline\n",
    "# S&P 500: ^GSPC\n",
    "# Dow Jones Index: ^DJI\n",
    "# NASDAQ 100: ^NDX\n",
    "backtest_plot(df_account_value, \n",
    "             baseline_ticker = '^DJI', \n",
    "             baseline_start = df_account_value.loc[0,'date'],\n",
    "             baseline_end = df_account_value.loc[len(df_account_value)-1,'date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "BzBaE63H3RLc"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "ZYeOjax-7H_5"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [
    "Uy5_PTmOh1hj",
    "_gDkU-j-fCmZ",
    "3Zpv4S0-fDBv"
   ],
   "include_colab_link": true,
   "name": "FinRL_StockTrading_NeurIPS_2018.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3.8.5 ('base')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
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